Neural networks have shown remarkable performance in computer vision, but their deployment in real-world scenarios is challenging due to their sensitivity to image corruptions. Existing attribution methods are uninformative for explaining the sensitivity to image corruptions, while the literature on robustness only provides model-based explanations. However, the ability to scrutinize models' behavior under image corruptions is crucial to increase the user's trust. Towards this end, we introduce the Wavelet sCale Attribution Method (WCAM), a generalization of attribution from the pixel domain to the space-scale domain. Attribution in the space-scale domain reveals where and on what scales the model focuses. We show that the WCAM explains models' failures under image corruptions, identifies sufficient information for prediction, and explains how zoom-in increases accuracy.

翻译：神经网络在计算机视觉中表现出卓越的性能，但由于其对图像破坏的敏感性，其在真实场景中的部署面临挑战。现有的归因方法无法有效解释对图像破坏的敏感性，而关于鲁棒性的文献仅提供基于模型的解释。然而，审视模型在图像破坏下的行为对于增强用户信任至关重要。为此，我们提出小波尺度归因方法（WCAM），将归因从像素域推广到空间-尺度域。空间-尺度域中的归因揭示了模型关注的位置和尺度。我们证明，WCAM能够解释模型在图像破坏下的失效情况，识别预测所需的充分信息，并解释放大如何提高准确率。